{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  一、案例简介\n",
    "## 1. 案例背景\n",
    ">   随着时代的进步，航空航天作为我国的战略性发展事业，取得了极大成就。航天事业的发展核心是飞行安全，飞行安全既是飞行人员与乘客的生命安全保障，又是航天科技发展的方向和目标。气象条件对飞行的影响是不同的，也是不可避免的，航天部门应对飞行威胁较大的恶劣天气进行分析，采取相应的对策进行防范，不断提高飞行的安全性与可靠性。\n",
    ">   恶劣天气的类型很多，主要包括大风、云、雷电、暴雨、冰雪，另外，大雾、风切变、冰高空急流等也会对飞行安全产生不同程度的影响。恶劣天气的影响具有不确定性，因而应减少在恶劣天气范围内进行飞行。\n",
    ">   通过天气因素上座数量的分析，预测不同天气情况下航班的上座情况，进而调整飞行计划以应对不同天气带来的影响。\n",
    "\n",
    "## 2. 案例意义\n",
    ">   从分析结果将使航空公司依据天气模式调整飞行时间表。它也可以引导乘客做出新的选择。航空公司可以提前几天预测到未来航班上座情况，然后未雨绸缪地进行调整。航空公司也可以预先发出警告更有效分配自己的资源、地勤人员、飞行人员和其他资产以减少损失\n",
    "## 3. 业务图\n",
    "![业务图](image-001.png)\n",
    "## 4. 案例目标\n",
    ">     现在，大多数航班只能对连续进行重复，一般处理天气延误的航班是在其发生之后。我们的大数据能够让他们预先预测延迟，以更好地与乘客沟通，以及优化资源。通过数据分析，预测假设航班和不同天气情况之间的结果，帮助迅速提前发现因天气发生的上座影响。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、相关技术\n",
    "- ## 数据采集，通过pymysql连接mysql数据库获取数据 \n",
    "- ## 数据预处理，通过pandas、sklearn进行数据清洗，转换等预处理\n",
    "- ## 数据探索，通过matplotlib及pyecharts进行数据可视化，发现数据规律\n",
    "- ## 数据分析建模，通过机器学习算法库sklearn学习已有数据规律建模，预测指定航班在不同天气下的上座率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、案例步骤"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 1. 数据采集: 从数据库casepro中获取数据表t_plane_order\\t_plane_weather,将表格转存在本地csv并展示数据\n",
    "\n",
    "> 数据库信息： host：10.102.52.248，port=3306， user=\"root\", passwd=\"root\"\n",
    "\n",
    "> 航班订单数据，包含航班时刻表，航班号，子订单，飞行日期，头等舱，公务舱，经济舱，其他，头等舱总数，公务舱总数，经济舱总数，其他总数。\n",
    "\n",
    "> 天气数据，包含基线：日期，高温，低温，天气状况，风，空气"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymysql #导入模块\n",
    "import pandas as pd\n",
    "\n",
    "#远程连接数据库\n",
    "db = pymysql.connect(\n",
    "         host='10.102.52.248',\n",
    "         port=3306,\n",
    "         user='root',\n",
    "         passwd='root',\n",
    "         db='casepro',\n",
    "         charset='utf8'\n",
    "         )\n",
    "def link_mysql(db):\n",
    "    #使用cursor()方法获取操作游标 \n",
    "    cursor = db.cursor()\n",
    "    sql = \"\"\"SELECT * FROM `t_plane_order`\"\"\"\n",
    "    sql1 = \"\"\"SELECT * FROM `t_plane_weather`\"\"\"\n",
    "    try:\n",
    "        cursor.execute(sql)  # 执行sql语句\n",
    "        result = cursor.fetchall()\n",
    "        columns = ['航班时刻表','航班号','子订单','飞行日期','头等舱','公务舱','经济舱','其他','头等舱总数','公务舱总数','经济舱总数','其他总数']\n",
    "        frame = pd.DataFrame(list(result),columns=columns)\n",
    "        frame.to_csv(\"航班天气因素上座情况预测分析案例.csv\",encoding='utf-8')\n",
    "\n",
    "        cursor.execute(sql1)  # 执行sql语句\n",
    "        result1 = cursor.fetchall()\n",
    "        columns_weather = ['日期','高温','低温','天气状况','风','空气']\n",
    "        frame_weather = pd.DataFrame(list(result1),columns=columns_weather)\n",
    "        frame_weather.to_csv(\"天气数据.csv\",encoding='utf-8')\n",
    "    except Exception:\n",
    "        db.rollback()  # 发生错误时回滚\n",
    "        print(\"查询失败\")\n",
    "\n",
    "    cursor.close()  # 关闭游标\n",
    "    db.close()  # 关闭数据库连接\n",
    "link_mysql(db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 2、数据预处理（清洗、数值化转换、标准化等）\n",
    "\n",
    "> 空数据判断和处理（注意分析空占比，区分数值型和类别型）\n",
    "\n",
    "> 数据规范性检查（格式、范围等）\n",
    "\n",
    "> 去除不规范数据、无效、无分析价值数据等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zyq\\AppData\\Local\\Temp/ipykernel_10472/1235738335.py:50: DtypeWarning: Columns (3,5) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  df_result = pd.merge(left=yucli_plane(), right=yucli_weath(), on=\"日期\", how=\"inner\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "各字段的空占比为：\n",
      "子订单      0.272959\n",
      "航班号      0.013529\n",
      "日期       0.000389\n",
      "航班时刻表    0.000000\n",
      "头等舱      0.000000\n",
      "公务舱      0.000000\n",
      "经济舱      0.000000\n",
      "其他       0.000000\n",
      "头等舱总数    0.000000\n",
      "公务舱总数    0.000000\n",
      "经济舱总数    0.000000\n",
      "其他总数     0.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zyq\\AppData\\Local\\Temp/ipykernel_10472/1235738335.py:34: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_plane_notNu.iloc[:,i][df_plane_notNu.iloc[:,i] < 0] = 0.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#对航班数据进行清理\n",
    "def yucli_plane():\n",
    "    df_plane = pd.read_csv('航班天气因素上座情况预测分析案例.csv',encoding='utf-8')\n",
    "    df_plane = pd.DataFrame(data=df_plane)\n",
    "    df_plane = df_plane.drop(columns='Unnamed: 0')\n",
    "    #判断其空占比\n",
    "    df_plane_percent = df_plane.isna().sum().sort_values(ascending=False) / len(df_plane)\n",
    "    print(\"各字段的空占比为：\\n{}\".format(df_plane_percent))\n",
    "\n",
    "    #将子订单的非数字改为0\n",
    "    df_plane.iloc[:,2] = np.where(df_plane.iloc[:,2].str.isdigit() == False,'非数字',df_plane.iloc[:,2])\n",
    "    df_plane = df_plane[df_plane.iloc[:,2] != '非数字']\n",
    "\n",
    "    #将子订单的空值替换为0\n",
    "    df_plane.iloc[:,2].fillna('0.0', inplace = True)\n",
    "    df_plane.iloc[:,2] = df_plane.iloc[:,2].astype(float)\n",
    "\n",
    "    #将不规范的航班号删除\n",
    "    df_plane.iloc[:,1] = np.where(df_plane.iloc[:,1].str.isalnum() !=  True,'不规范',df_plane.iloc[:,1])\n",
    "    df_plane = df_plane[df_plane.iloc[:,1] != '不规范']\n",
    "    \n",
    "    #删除空数据\n",
    "    df_plane_notNu = df_plane.dropna(axis=0)\n",
    "    #删除重复数据\n",
    "    df_plane_notNu = df_plane_notNu.drop_duplicates()\n",
    "    #将str类型改为float\n",
    "    for i in range(4,12):\n",
    "        df_plane_notNu.iloc[:,i] = df_plane_notNu.iloc[:,i].astype(float)\n",
    "    #将负值替换成零\n",
    "    for i in range(4,12):\n",
    "        df_plane_notNu.iloc[:,i][df_plane_notNu.iloc[:,i] < 0] = 0.0\n",
    "    #删除日期中不规范的数据\n",
    "    df_plane_notNu[df_plane_notNu.iloc[:,3] == \"0.0\"] = np.nan\n",
    "    df_plane_notNu.dropna(axis=0)\n",
    "    return df_plane_notNu\n",
    "\n",
    "#对天气数据进行处理\n",
    "def yucli_weath():\n",
    "    df_weath = pd.read_csv('天气数据.csv',encoding='utf-8')\n",
    "    df_weath = pd.DataFrame(df_weath)\n",
    "    #删除无分析价值数据\n",
    "    df_weath = df_weath.drop('Unnamed: 0',axis=1)\n",
    "    #将日期和空气格式化\n",
    "    df_weath['日期'] = df_weath['日期'].apply(lambda x:str(x)[0:10])\n",
    "    df_weath['空气'] = df_weath['空气'].apply(lambda x:str(x)[:-1])\n",
    "    return df_weath\n",
    "df_result = pd.merge(left=yucli_plane(), right=yucli_weath(), on=\"日期\", how=\"inner\")\n",
    "#df_result.groupby(['日期','航班号','天气状况','风','空气']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 3、数据探索，发现数据中的规律"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import lineStyles\n",
    "import seaborn as sns\n",
    "from pyecharts.charts import Pie\n",
    "from pyecharts import options as opts\n",
    "#sklearn 需要的特征值\n",
    "def yc(df_result):\n",
    "    df_result = df_result.replace(['中雨~小雨','多云~中雨','多云~小雨','多云~晴','多云~阴','多云~雷阵雨','多云~霾','大暴雨~中雨','大雨~多云','小雨~中雨','小雨~多云','小雨~大雨','小雨~小到中雨','小雨~晴','小雨~阴','小雪~多云','小雪~阴','晴~多云','晴~小雨','晴~阴','晴~霾','阴~多云','阴~小雨','阴~晴','阴~阵雨','阴~雷阵雨','阵雨~多云','阵雨~阴','雨夹雪~大雪','雷阵雨~阵雨','霾~多云','霾~小雨','霾~晴','霾~阴','霾~阵雨','霾~雨夹雪','霾~雷阵雨','霾~雾'],['小雨','中雨','小雨','晴','阴','雷阵雨','霾','中雨','中雨','小雨','小雨','中雨','小雨','晴','阴','多云','阴','晴','晴','阴','霾','阴','小雨','阴','阴','雷阵雨','阵雨','阵雨','大雪','雷阵雨','多云','小雨','晴','阴','阵雨','雨夹雪','雷阵雨','雾'])\n",
    "    df_result['上座人数']=df_result[['公务舱','头等舱','经济舱','其他']].sum(axis=1)\n",
    "    df_result['总座位数']=df_result[['头等舱总数','公务舱总数','经济舱总数','其他总数']].sum(axis=1)\n",
    "    y1=(df_result['上座人数']/df_result['总座位数'])\n",
    "    df_result['上座率'] = y1\n",
    "    #清除上座率为0的数据\n",
    "    df_result = df_result[df_result.iloc[:,19] != 0.0]\n",
    "    #删除上座率错误的数据\n",
    "    df_result.iloc[:,19] = np.where(df_result.iloc[:,19] == np.inf,'错误数据',df_result.iloc[:,19])\n",
    "    df_result = df_result[df_result.iloc[:,19] != '错误数据']\n",
    "    #删除上座率为空的数据\n",
    "    df_result = df_result.dropna(axis=0)\n",
    "    \n",
    "    data = pd.concat([df_result['天气状况'],df_result['高温'],df_result['低温'],df_result['上座率']],axis=1)\n",
    "    data.iloc[:,3] = data.iloc[:,3].astype(float)\n",
    "    data = data.groupby(['天气状况','高温','低温'],as_index=False).mean()\n",
    "    data['上座率'] = data['上座率'].apply(lambda x: format(x, '.2'))\n",
    "    data.to_csv('根据天气状况特征预测.csv')\n",
    "\n",
    "#根据空气统计航班各舱的情况\n",
    "def show_air_rain(df_result):\n",
    "    #按空气聚合'头等舱','公务舱','经济舱'\n",
    "    df_air_cabin=df_result.groupby('空气')['头等舱','公务舱','经济舱','其他'].sum()\n",
    "    plt.rcParams['font.sans-serif']=['SimHei']\n",
    "    x=df_air_cabin.index.values\n",
    "    y1=df_air_cabin[['头等舱']]\n",
    "    y2=df_air_cabin[['公务舱']]\n",
    "    y3=df_air_cabin[['经济舱']]\n",
    "    y4=df_air_cabin[['其他']]\n",
    "    #画图\n",
    "    plt.plot(x,y1,marker='p')\n",
    "    plt.plot(x,y2,marker='p')\n",
    "    plt.plot(x,y3,marker='p')\n",
    "    plt.plot(x,y4,marker='p')\n",
    "    #plt.xticks(x,\"\")\n",
    "    plt.xlabel(\"空气\")\n",
    "    plt.ylabel(\"上座数(单位：百万)\")\n",
    "    plt.title(\"航班的头等舱、公务舱、经济舱以及其他舱在不同空气等级下的购买数\")\n",
    "    #设置数字标签\n",
    "    #头等舱\n",
    "    for a, b in zip(x, y1.values):\n",
    "        plt.text(a, b, b, ha='center', va='bottom', fontsize=9,rotation=20)\n",
    "    #经济舱\n",
    "    for a, b in zip(x, y3.values):\n",
    "        plt.text(a, b, b, ha='center', va='bottom', fontsize=9,rotation=5)\n",
    "    #公务舱\n",
    "    for a, b in zip(x, y2.values):\n",
    "        plt.text(a, b, b, ha='center', va='top', fontsize=9) \n",
    "    #其他\n",
    "    for a, b in zip(x, y4.values):\n",
    "        plt.text(a, b, b, ha='center', va='top', fontsize=9)  \n",
    "    tuli=['头等舱','公务舱','经济舱','其他']\n",
    "    plt.legend(tuli)\n",
    "    plt.show()\n",
    "\n",
    "#根据空气统计航班的上座率\n",
    "def show_air_lv(df_result):\n",
    "    #按空气聚合'头等舱','公务舱','经济舱'\n",
    "    df_air_cabin=df_result.groupby('空气')['头等舱','公务舱','经济舱','其他'].sum()\n",
    "    #空气——'头等舱总数','公务舱总数','经济舱总数'\n",
    "    df_air_cabin_sum=df_result.groupby('空气')['头等舱总数','公务舱总数','经济舱总数','其他'].sum()\n",
    "    #'头等舱'+'公务舱'+'经济舱' + '其他'=\"购票数\"\n",
    "    book=df_air_cabin[\"头等舱\"].values+df_air_cabin[\"公务舱\"].values+df_air_cabin[\"经济舱\"].values + df_air_cabin['其他'].values\n",
    "    #'头等舱总数'+'公务舱总数'+'经济舱总数'+ '其他'='上座位'\n",
    "    cabin_sum=df_air_cabin_sum[\"头等舱总数\"].values+df_air_cabin_sum[\"公务舱总数\"].values+df_air_cabin_sum[\"经济舱总数\"].values+ df_air_cabin_sum['其他'].values\n",
    "    seat_x=df_air_cabin_sum.index\n",
    "    seat_y=cabin_sum\n",
    "    book_y=book\n",
    "    #上座率\n",
    "    #seat_ratio=seat_y/book_y\n",
    "    seat_ratio=book_y/seat_y\n",
    "    #只保留小数点后两位的数据\n",
    "    plt.xlabel(\"空气等级\")\n",
    "    plt.ylabel(\"上座率\")\n",
    "    plt.title(\"航班的头等舱、公务舱、经济舱在不同空气等级下的上座率\")\n",
    "\n",
    "    for a, b in zip(seat_x, seat_ratio):\n",
    "        plt.text(a, b, b, ha='center', va='bottom', fontsize=9,rotation=20)\n",
    "    plt.legend(\"上座率\")\n",
    "    plt.plot(seat_x,seat_ratio)\n",
    "\n",
    "#根据风统计航班各舱的情况\n",
    "def show_win_rain(df_result):\n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']#用来正常显示中文标签\n",
    "    plt.rcParams['axes.unicode_minus'] = False#用来正常显示负号\n",
    "    figure=plt.figure(figsize=(30,20),dpi=160)\n",
    "    df_result = df_result.replace(['东北风微风','东南风微风','东风微风','北风微风','南风微风','西北风微风','西南风微风','西风微风'],'微风')\n",
    "    df_result = df_result.replace(['东北风1-2级','东南风1-2级','东风1-2级','北风1-2级','南风1-2级','西北风1-2级','西南风1-2级','西风1-2级'],'1-2级风')\n",
    "    df_result = df_result.replace(['东风3-4级','北风3-4级','南风3-4级','西北风3-4级'],'3-4级风')\n",
    "    df_result = df_result.replace('北风4-5级','4-5级风')\n",
    "    df_result1=df_result.groupby('风')['头等舱','公务舱','经济舱'].sum()\n",
    "    x_ticks=df_result1.index.values\n",
    "    x = np.arange(len(x_ticks))\n",
    "    plt.xticks(x, x_ticks)\n",
    "    y1=df_result1['头等舱'].values\n",
    "    y2=df_result1['公务舱'].values\n",
    "    y3=df_result1['经济舱'].values\n",
    "\n",
    "    plt.xlabel('风')\n",
    "    plt.ylabel('上座率')\n",
    "    plt.title('航班的头等舱、公务舱、经济舱在不同风下的上座率')\n",
    "\n",
    "\n",
    "    width =0.25\n",
    "    plt.bar(x,y1,width=width,color=\"blue\")\n",
    "    plt.bar(x+width,y2,width=width,color=\"red\")\n",
    "    plt.bar(x+2*width,y3,width=width,color=\"orange\")\n",
    "    \n",
    "    plt.legend(['头等舱','公务舱','经济舱'])#显示图例\n",
    "    plt.show()\n",
    "\n",
    "#根据风统计航班的上座率\n",
    "def show_win_lv(df_result):\n",
    "    figure=plt.figure(figsize=(10,10),dpi=100)\n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']#用来正常显示中文标签\n",
    "    plt.rcParams['axes.unicode_minus'] = False#用来正常显示负号\n",
    "    df_result = df_result.replace(['东北风微风','东南风微风','东风微风','北风微风','南风微风','西北风微风','西南风微风','西风微风'],'微风')\n",
    "    df_result = df_result.replace(['东北风1-2级','东南风1-2级','东风1-2级','北风1-2级','南风1-2级','西北风1-2级','西南风1-2级','西风1-2级'],'1-2级风')\n",
    "    df_result = df_result.replace(['东风3-4级','北风3-4级','南风3-4级','西北风3-4级'],'3-4级风')\n",
    "    df_result = df_result.replace('北风4-5级','4-5级风')\n",
    "    df_result2=df_result.groupby('风').sum()\n",
    "    # df_result2\n",
    "    x_ticks=df_result2.index.values\n",
    "    x = np.arange(len(x_ticks))\n",
    "    plt.xticks(x, x_ticks)\n",
    "    plt.xticks(fontsize=5)\n",
    "    plt.yticks(fontsize=8) \n",
    "    con1=df_result2['头等舱'].values+df_result2['公务舱'].values+df_result2['经济舱'].values+df_result2['其他'].values\n",
    "    con2=df_result2['头等舱总数'].values+df_result2['公务舱总数'].values+df_result2['经济舱总数'].values+df_result2['其他总数'].values\n",
    "    width =0.25\n",
    "    plt.bar(x,con1/con2,width=width,color=\"orange\")\n",
    "    plt.title(\"根据风统计航班的上座率\")\n",
    "    plt.legend(['上座率','其他总数'])#显示图例\n",
    "    plt.show()\n",
    "\n",
    "#根据高温统计航班各舱的情况\n",
    "def show_weath_height(df_result):\n",
    "    plt.rcParams['font.sans-serif']=['Simhei']\n",
    "    plt.rcParams['axes.unicode_minus']=False\n",
    "    df=df_result.groupby('高温',as_index=False).sum()\n",
    "    x=df['高温']\n",
    "    y=df['头等舱']\n",
    "    y1=df['公务舱']\n",
    "    y2=df['经济舱']\n",
    "    width=0.3\n",
    "    plt.figure(figsize=(20, 8), dpi=100)\n",
    "    plt.xticks(np.arange(-15,40))\n",
    "    plt.xlabel('高温温度(摄氏度)')\n",
    "    plt.ylabel('头等舱数量')\n",
    "    plt.title('高温对各舱数量影响分析图')\n",
    "    plt.bar(x,y,width=width,color='green') #紫罗兰颜色\n",
    "    plt.bar(x+width,y1,width=width,color='blue') #海军蓝颜色\n",
    "    plt.bar(x+2*width,y2,width=width,color='red') #浅海蓝绿\n",
    "    plt.legend(['头等舱','公务舱','经济舱'])\n",
    "    plt.show()\n",
    "\n",
    "#根据低温统计航班各舱的情况\n",
    "def show_weath_low(df_result):\n",
    "    plt.rcParams['font.sans-serif']=['Simhei']\n",
    "    plt.rcParams['axes.unicode_minus']=False\n",
    "    df=df_result.groupby('低温',as_index=False).sum()\n",
    "    x=df['低温']\n",
    "    y=df['头等舱']\n",
    "    y1=df['公务舱']\n",
    "    y2=df['经济舱']\n",
    "    width=0.3\n",
    "    plt.figure(figsize=(20,8), dpi=100)\n",
    "    plt.xlabel('低温温度(摄氏度)')\n",
    "    plt.ylabel('各舱数量')\n",
    "    plt.xticks(np.arange(-18,25))\n",
    "    plt.title('低温对各舱数量影响分析图')\n",
    "    plt.bar(x,y,width=width,color='#00FFFF') #青色\n",
    "    plt.bar(x+width,y1,width=width,color='#FFA500') #橙色\n",
    "    plt.bar(x+2*width,y2,width=width,color='pink') #粉色\n",
    "    plt.legend(['头等舱','公务舱','经济舱'])\n",
    "    plt.show()\n",
    "\n",
    "#根据温度统计航班的上座率\n",
    "def show_weath_lv(df_result):\n",
    "    plt.rcParams['font.sans-serif']=['Simhei']\n",
    "    plt.rcParams['axes.unicode_minus']=False\n",
    "    df=df_result.groupby('高温',as_index=False).sum()\n",
    "    df1=df.iloc[:,2:6].sum(axis=1)\n",
    "    df1_1=df.iloc[:,6:].sum(axis=1)\n",
    "    rate_high=df1/df1_1\n",
    "    # rate_high\n",
    "    # print(np.array(df1))\n",
    "    # df1=df['头等舱']+df['公务舱']+df['经济舱']+df['其他']\n",
    "    # df1\n",
    "    df2=df_result.groupby('低温',as_index=False).sum()\n",
    "    df3=df2.iloc[:,2:6].sum(axis=1)\n",
    "    df3_3=df2.iloc[:,6:].sum(axis=1)\n",
    "    rate_low=df3/df3_3\n",
    "    # # df2\n",
    "    figure=plt.figure(figsize=(20,8),dpi=100)\n",
    "    x=df['高温']\n",
    "    plt.xticks(np.arange(-20,40))\n",
    "    x1=df2['低温']\n",
    "    y=rate_high\n",
    "    y1=rate_low\n",
    "    plt.xlabel('温度(摄氏度)',fontsize=16)\n",
    "    plt.ylabel('上坐率',fontsize=16)\n",
    "    plt.title('温度对上座率影响分析图')\n",
    "    plt.plot(x,y,color='red')\n",
    "    plt.plot(x1,y1,color='blue')\n",
    "    plt.legend(['高温','低温'])\n",
    "    plt.show()\n",
    "\n",
    "def show_weather_rain(df_result):\n",
    "    plt.figure(figsize=(20,10),dpi=180)\n",
    "    plt.rcParams['font.sans-serif']=['SimHei']\n",
    "    #把所有舱人数总和\n",
    "    df_result = df_result.replace(['中雨~小雨','多云~中雨','多云~小雨','多云~晴','多云~阴','多云~雷阵雨','多云~霾','大暴雨~中雨','大雨~多云','小雨~中雨','小雨~多云','小雨~大雨','小雨~小到中雨','小雨~晴','小雨~阴','小雪~多云','小雪~阴','晴~多云','晴~小雨','晴~阴','晴~霾','阴~多云','阴~小雨','阴~晴','阴~阵雨','阴~雷阵雨','阵雨~多云','阵雨~阴','雨夹雪~大雪','雷阵雨~阵雨','霾~多云','霾~小雨','霾~晴','霾~阴','霾~阵雨','霾~雨夹雪','霾~雷阵雨','霾~雾'],['小雨','中雨','小雨','晴','阴','雷阵雨','霾','中雨','中雨','小雨','小雨','中雨','小雨','晴','阴','多云','阴','晴','晴','阴','霾','阴','小雨','阴','阴','雷阵雨','阵雨','阵雨','大雪','雷阵雨','多云','小雨','晴','阴','阵雨','雨夹雪','雷阵雨','雾'])\n",
    "    df_result1=df_result.groupby('天气状况').sum()\n",
    "    df_result1['上座人数']=df_result1[['公务舱','头等舱','经济舱','其他']].sum(axis=1)\n",
    "    df_result1\n",
    "    x=df_result1.index.values\n",
    "\n",
    "    y2=df_result1['上座人数']\n",
    "    sns.barplot(x,y2,label='上座人数(单位：百万)')\n",
    "    plt.show()\n",
    "    df_result1['总座位数']=df_result1[['头等舱总数','公务舱总数','经济舱总数','其他总数']].sum(axis=1)\n",
    "    y1=(df_result1['上座人数']/df_result1['总座位数'])\n",
    "    sns.lineplot(x,y1,label='上座率')\n",
    "    df = pd.DataFrame(x,y1)\n",
    "    plt.show()\n",
    "def show_weather_pie(df_result):\n",
    "    #把所有舱人数总和\n",
    "    df_result = df_result.replace(['中雨~小雨','多云~中雨','多云~小雨','多云~晴','多云~阴','多云~雷阵雨','多云~霾','大暴雨~中雨','大雨~多云','小雨~中雨','小雨~多云','小雨~大雨','小雨~小到中雨','小雨~晴','小雨~阴','小雪~多云','小雪~阴','晴~多云','晴~小雨','晴~阴','晴~霾','阴~多云','阴~小雨','阴~晴','阴~阵雨','阴~雷阵雨','阵雨~多云','阵雨~阴','雨夹雪~大雪','雷阵雨~阵雨','霾~多云','霾~小雨','霾~晴','霾~阴','霾~阵雨','霾~雨夹雪','霾~雷阵雨','霾~雾'],['小雨','中雨','小雨','晴','阴','雷阵雨','霾','中雨','中雨','小雨','小雨','中雨','小雨','晴','阴','多云','阴','晴','晴','阴','霾','阴','小雨','阴','阴','雷阵雨','阵雨','阵雨','大雪','雷阵雨','多云','小雨','晴','阴','阵雨','雨夹雪','雷阵雨','雾'])\n",
    "    df_result1=df_result.groupby('天气状况').sum()\n",
    "    df_result1['上座人数']=df_result1[['公务舱','头等舱','经济舱','其他']].sum(axis=1)\n",
    "    df_result1\n",
    "    x_data=df_result1.index.values\n",
    "\n",
    "    y_data=df_result1['上座人数']\n",
    "\n",
    "    data = [list(z) for z in zip(x_data,y_data)]\n",
    "    data.sort(key=lambda x:x[1])\n",
    "    pie= Pie()\n",
    "    pie.add(\n",
    "        series_name = '',\n",
    "        data_pair=data,\n",
    "    )\n",
    "    pie.set_global_opts(\n",
    "        title_opts=opts.TitleOpts(\n",
    "            title=\"上座人数\",\n",
    "            pos_left=\"center\"\n",
    "        ),\n",
    "        legend_opts=opts.LegendOpts(is_show=False),\n",
    "    )\n",
    "    pie.set_series_opts(\n",
    "        label_opts=opts.LabelOpts(),\n",
    "    )\n",
    "    return pie\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zyq\\AppData\\Local\\Temp/ipykernel_10472/3406525938.py:30: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_air_cabin=df_result.groupby('空气')['头等舱','公务舱','经济舱','其他'].sum()\n",
      "d:\\anaconda3\\lib\\site-packages\\matplotlib\\text.py:1215: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if s != self._text:\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zyq\\AppData\\Local\\Temp/ipykernel_10472/3406525938.py:66: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_air_cabin=df_result.groupby('空气')['头等舱','公务舱','经济舱','其他'].sum()\n",
      "C:\\Users\\zyq\\AppData\\Local\\Temp/ipykernel_10472/3406525938.py:68: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_air_cabin_sum=df_result.groupby('空气')['头等舱总数','公务舱总数','经济舱总数','其他'].sum()\n",
      "C:\\Users\\zyq\\AppData\\Local\\Temp/ipykernel_10472/3406525938.py:98: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_result1=df_result.groupby('风')['头等舱','公务舱','经济舱'].sum()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4800x3200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3600x1800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"2f2d42aadaa5468daec89e75142d6606\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_2f2d42aadaa5468daec89e75142d6606 = echarts.init(\n",
       "                    document.getElementById('2f2d42aadaa5468daec89e75142d6606'), 'white', {renderer: 'canvas'});\n",
       "                var option_2f2d42aadaa5468daec89e75142d6606 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"pie\",\n",
       "            \"clockwise\": true,\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u96fe\",\n",
       "                    \"value\": 58430.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5927\\u96ea\",\n",
       "                    \"value\": 79874.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u96e8\\u5939\\u96ea\",\n",
       "                    \"value\": 223954.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e2d\\u96e8\",\n",
       "                    \"value\": 362326.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9635\\u96e8\",\n",
       "                    \"value\": 511691.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u96f7\\u9635\\u96e8\",\n",
       "                    \"value\": 588288.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u973e\",\n",
       "                    \"value\": 2656772.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5c0f\\u96e8\",\n",
       "                    \"value\": 4603885.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u9634\",\n",
       "                    \"value\": 5635209.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u591a\\u4e91\",\n",
       "                    \"value\": 7351446.0\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6674\",\n",
       "                    \"value\": 8210689.0\n",
       "                }\n",
       "            ],\n",
       "            \"radius\": [\n",
       "                \"0%\",\n",
       "                \"75%\"\n",
       "            ],\n",
       "            \"center\": [\n",
       "                \"50%\",\n",
       "                \"50%\"\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u96fe\",\n",
       "                \"\\u5927\\u96ea\",\n",
       "                \"\\u96e8\\u5939\\u96ea\",\n",
       "                \"\\u4e2d\\u96e8\",\n",
       "                \"\\u9635\\u96e8\",\n",
       "                \"\\u96f7\\u9635\\u96e8\",\n",
       "                \"\\u973e\",\n",
       "                \"\\u5c0f\\u96e8\",\n",
       "                \"\\u9634\",\n",
       "                \"\\u591a\\u4e91\",\n",
       "                \"\\u6674\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": false,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u4e0a\\u5ea7\\u4eba\\u6570\",\n",
       "            \"left\": \"center\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_2f2d42aadaa5468daec89e75142d6606.setOption(option_2f2d42aadaa5468daec89e75142d6606);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1c688c5f610>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_air_rain(df_result)\n",
    "show_air_lv(df_result)\n",
    "show_win_rain(df_result)\n",
    "show_win_lv(df_result)\n",
    "show_weath_height(df_result)\n",
    "show_weath_low(df_result)\n",
    "show_weath_lv(df_result)\n",
    "show_weather_rain(df_result)\n",
    "show_weather_pie(df_result).render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 4、数据分析建模并评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_squared_error,mean_absolute_error  # 评价指标\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "data = pd.read_csv('根据天气状况特征预测.csv')\n",
    "data = data.replace(['中雨','多云','大雪','小雨','晴','阴','阵雨','雨夹雪','雷阵雨','雾','霾'],[0,1,2,3,4,5,6,7,8,9,10])\n",
    "x_train, x_test, y_train, y_test = train_test_split(np.array(data.iloc[:,1:4]),np.array(data['上座率']),test_size=0.3)\n",
    "#离差标准化\n",
    "Scaler = MinMaxScaler().fit(x_train)\n",
    "x_train = Scaler.transform(x_train)\n",
    "x_test = Scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================knn算法=========================\n",
      "预测的上座率为：[0.386 0.38  0.376 0.38  0.364 0.38  0.36  0.354 0.356 0.376]\n",
      "实际的上座率为：[0.35 0.39 0.4  0.33 0.44 0.41 0.31 0.33 0.33 0.35]\n",
      "均方误差为: 0.0010503673469387756\n",
      "平均均对误差: 0.02538775510204082\n",
      "knn的平均准确度为: -0.0011540179236015913\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD2CAYAAADcUJy6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPGElEQVR4nO3df4xVZX7H8fdXHQsB6yJOMFucBSIaN7hQMloRtaMRgk1/zuLa1Ehsd+N2Y2xI/ylGzNqWLulmS1iNqFRKNrY1lUaNdotQsVNohLZDF2m71qx/YAsRYQElKETSfPvHXASGmZF75seZeXy/kknO9T7nPp8B+cwzzz1zJjITSVJ5Lqg7gCRpeFjwklQoC16SCmXBS1KhLHhJKtRFdQc45fLLL89p06bVHUOSxpSdO3f+NDNb+3pu1BT8tGnT6O7urjuGJI0pEfFuf8+5RSNJhbLgJalQFrwkFcqCl6RCWfCSVCgLXpIKZcFLUqFGzXXwlS1dCrt21Z1CkqqbMwdWrx7yl3UFL0mFGvsr+GH4qidJJXAFL0mFsuAlqVAWvCQVyoKXpEJZ8JJUKAtekgplwUtSoSx4SSqUBS9JhbLgJalQFrwkFcqCl6RCWfCSVCgLXpIKZcFLUqEseEkqlAUvSYWy4CWpUBa8JBXKgpekQlnwklQoC16SCmXBS1KhLHhJKpQFL0mFsuAlqVAWvCQVyoKXpEJVLviIWBcR2yNieT/PXxoRGyNic0S8GBEXV48pSWpWpYKPiE7gwsycB8yIiJl9DLsHWJWZC4H9wKLqMSVJzbqo4nkdwPON483AzcBPzhyQmWvOeNgKHOj9IhFxP3A/QFtbW8UokqS+VN2imQDsaxwfBqb0NzAi5gGTMnNH7+cyc21mtmdme2tra8UokqS+VF3BHwPGN44n0s8Xioi4DHgc+GrFeSRJFVVdwe+kZ1sGYDawp/eAxpuqG4CHMvPdivNIkiqqWvAvAfdGxCrga8B/RcSKXmO+DswFHo6Iroi4u3pMSVKzKm3RZObRiOgAFgDfzcz9wJu9xjwJPDnYgJKkaqruwZOZRzh9JY0kaZTxJ1klqVAWvCQVyoKXpEJZ8JJUKAtekgplwUtSoSx4SSqUBS9JhbLgJalQFrwkFcqCl6RCWfCSVCgLXpIKZcFLUqEseEkqlAUvSYWy4CWpUBa8JBXKgpekQlnwklQoC16SCmXBS1KhLHhJKpQFL0mFsuAlqVAWvCQVyoKXpEJZ8JJUKAtekgplwUtSoSx4SSqUBS9JhbLgJalQFrwkFcqCl6RCWfCSVKjKBR8R6yJie0QsH8wYSdLwqFTwEdEJXJiZ84AZETGzyhhJ0vCpuoLvAJ5vHG8Gbq4yJiLuj4juiOg+ePBgxSiSpL5ULfgJwL7G8WFgSpUxmbk2M9szs721tbViFElSX6oW/DFgfON4Yj+vcz5jJEnDpGrp7uT0lstsYE/FMZKkYXJRxfNeArZFxBeBO4HfjIgVmbl8gDE3DiaoJKk5lVbwmXmUnjdRdwC3Zeabvcq9rzEfDi6qJKkZVVfwZOYRTl8lU3mMJGl4+ManJBWq8gpekup08uRJ9u7dy4kTJ+qOMiLGjRvH1KlTaWlpOe9zLHhJY9LevXu55JJLmDZtGhFRd5xhlZkcOnSIvXv3Mn369PM+zy0aSWPSiRMnmDx5cvHlDhARTJ48uenvVix4SWPW56HcT6nyuVrwklTRrl272LVrV6Vzly5dOqRZ+mLBS1JFgyn41atXD2mWvvgmq6Sxb+lSqFi0/ZozBwYo4YceeogXX3wRgGeffZYtW7bQ0dHB9ddfz+7du9m0aRPHjh1j8eLFfPTRR1x11VWsX7/+0/M7Ojro6uoC4NFHH+XkyZNs27aNo0eP8uqrr3LFFVcM+lNwBS9JFaxcuZJly5axbNkytmzZAsCOHTuYN28emzZtAuC9997jwQcf5LXXXmPPnj28//77/b7eO++8w9atW+ns7OT1118fkoyu4CWNfSOw3XE+Zs2aRWdn56ePW1paeOaZZ1i/fj2HDx/m+PHj/Z67ZMkSANra2vjkk0+GJI8reEmqaPz48Xz88cdAz7XqEydOPOv5devWsXjxYp577jkmTJgw4Gt91vNVWPCSVNGCBQt44YUXmD9/Ptu2bevz+ZUrV3L77bcDsG/fvnPGDKfIzBGdsD/t7e3Z3d1ddwxJY8Rbb73FtddeW3eMEdXX5xwROzOzva/xruAlqVAWvCQVyoKXpEJZ8JJUKAtekgplwUtSRVXvRTOYe9g0w4KXpIpGe8F7qwJJY14N9xo752Zjr7zyCkuWLOHAgQNcd911PPHEExw/fpy77rqLo0ePMnnyZDZs2MAjjzxyzk3KhosFL0kVrFy5kmuuuQaA++67j9WrVzNr1iweffRROjs72b17NydPnuSCCy5g69atvPzyyxw7duyc84aTBS9pzBsN9xp7++23eeONN+jq6uKDDz5g3759LFq0iFmzZrFw4UJmzpzJokWLRjSTe/CSVNGZNxu7+uqrWbp0KV1dXaxYsYK2tjbefPNN5s+fz+bNmzly5Min96vpfZOy4WLBS1JFZ95sbO7cuWzcuJFbb72Vp556iiuvvJJp06bx2GOPcdNNN7F//37a29vPOa+vm5QNFW82JmlM8mZjPbzZmCR9DlnwklQoC17SmDVatphHQpXP1YKXNCaNGzeOQ4cOfS5KPjM5dOgQ48aNa+o8r4OXNCZNnTqVvXv3cvDgwbqjjIhx48YxderUps6x4CWNSS0tLUyfPr3uGKOaWzSSVCgLXpIKZcFLUqEseEkqVNMFHxHrImJ7RCwfYMylEbExIjZHxIsRcfHgYkqSmtVUwUdEJ3BhZs4DZkTEzH6G3gOsysyFwH5gZO+RKUlq+jLJDuD5xvFm4GbgJ70HZeaaMx62Agf6erGIuB+4H6Ctra3JKJKkgQxY8BHxNHDNGf/pF4F1jePDwNzPOH8eMCkzd/T1fGauBdZCz90kzzOzJOk8DFjwmfnNMx9HxPeB8Y2HExlgiyciLgMeB746yIySpAqafZN1Jz3bMgCzgT19DWq8qboBeCgz362cTpJUWbMF/xJwb0SsAr4G/DAivhwRK3qN+zo92zcPR0RXRNw9+KiSpGY0/RudImISsADYmpn7hyqIv9FJkpo30G90avpmY5l5hNNX0kiSRil/klWSCmXBS1KhLHhJKpQFL0mFsuAlqVAWvCQVyoKXpEJZ8JJUKAtekgplwUtSoSx4SSqUBS9JhbLgJalQFrwkFcqCl6RCWfCSVCgLXpIKZcFLUqEseEkqlAUvSYWy4CWpUBa8JBXKgpekQlnwklQoC16SCmXBS1KhLHhJKpQFL0mFsuAlqVAWvCQVyoKXpEJZ8JJUKAtekgplwUtSoSx4SSqUBS9JhWq64CNiXURsj4jl5zF2SkT8qFo0SdJgNFXwEdEJXJiZ84AZETHzM075HjC+ajhJUnXNruA7gOcbx5uBm/sbGBG3Ax8B+wcYc39EdEdE98GDB5uMIkkayIAFHxFPR0TXqQ/gQWBf4+nDwJR+zrsYeARYNtDrZ+bazGzPzPbW1tamw0uS+nfRQE9m5jfPfBwR3+f0lstE+v8CsQxYk5kfRMSgQ0qSmtfsFs1OTm/LzAb29DPuDuCBxqp/TkQ8UymdJKmyAVfwfXgJ2BYRXwTuBG6MiC8Dv5WZn15Vk5m3njqOiK7M/MZQhJUknb+mVvCZeZSeN1p3ALdl5oeZ+eMzy72PczoGlVCSVEmzK3gy8winr6SRJI1S/iSrJBXKgpekQlnwklQoC16SCmXBS1KhLHhJKpQFL0mFsuAlqVAWvCQVyoKXpEJZ8JJUKAtekgplwUtSoSx4SSqUBS9JhbLgJalQFrwkFcqCl6RCWfCSVCgLXpIKZcFLUqEseEkqlAUvSYW6qO4Ag7V0KezaVXcKSapuzhxYvXroX9cVvCQVasyv4Ifjq54klcAVvCQVyoKXpEJZ8JJUKAtekgplwUtSoSx4SSqUBS9JhbLgJalQkZl1ZwAgIg4C71Y8/XLgp0MYpypznM0cZxsNOUZDBjBHb4PJ8aXMbO3riVFT8IMREd2Z2W4Oc5hj9Gcwx8jlcItGkgplwUtSoUop+LV1B2gwx9nMcbbRkGM0ZABz9DYsOYrYg5cknauUFbwkqRcLvjARcVlELIiIy+vOIqleRRR8REyJiG01zn9pRGyMiM0R8WJEXFxTjknA3wE3AP8YEX1eGztCWaZExI9qnP+iiPifiOhqfFxXV5ZGnjUR8Ss1zv+tM/4sdkXE0zXlmBQRfx8R3XVlaOSYHhE/jIhtEfFnNWU4q7ciYl1EbI+I5UM1x5gv+Eap/QCYUGOMe4BVmbkQ2A8sqinHV4Dfz8w/ATYBc2vKAfA9YHyN838FeC4zOxof/1FXkIi4BbgiM1+pK0NmPnnqzwLYBvx5TVHuBf6qcc33JRFR1zXofwr8cWbeAkyNiI6RnLx3b0VEJ3BhZs4DZkTEzKGYZ8wXPPB/wN3A0boCZOaazPyHxsNW4EBNOf4pM3dExK30rOK315EjIm4HPqLni11dbgR+OSL+tbEyquXXU0ZECz1luicifq2ODL3y/BwwJTO7a4pwCJgVEV8ArgT+t6YcVwP/3jg+AFw6wvP37q0O4PnG8Wbg5qGYZMwXfGYezcwP684BEBHzgEmZuaPGDEHP/zhHgJM1zH8x8AiwbKTn7uXfgDsy8wagBfilmnIsAX4MfBe4ISIerCnHKQ8AT9Y4/z8DXwJ+D3gLOFxTjr8Fvt3YNlsEbBnJyfvorQnAvsbxYWDKUMwz5gt+tIiIy4DHgd+pM0f2eADYDfxqDRGWAWsy84Ma5j7T7sx8r3HcDQzJt7wV/DywNjP3A38J3FZTDiLigsb8XXVlAL4N/G5m/hHw38Bv1xEiM1cAG4FvAD/IzGN15DjDMU5vaU5kiLrZgh8CjVXrBuChzKx6w7ShyPEHEbGk8fALwAc1xLgDeCAiuoA5EfFMDRkAno2I2RFxIfDrwJs15XgHmNE4bqf6DfWGwi3Av2S9P/wyCbiu8ffyC0CdWXYBbcCqGjOcspPT2zKzgT1D8aLF/KBTRHQ13kCqY+5vAd/hdIk8mZl/U0OOSfTs4/0M8J/AA3X+Y67572QW8NdAAC9n5sM15bgE+At6vuVuARZn5r6Bzxq2LN8BujPzhTrmb2S4AVhPzzbNduA36lo9R8QfAu9k5rN1zN/I0JWZHRHxs/S8+b0FuBO4cSi2nospeEkayxoLtAXA1saW3uBf04KXpDK5By9JhbLgJalQFrwkFcqCl6RCWfCSVKj/B4kdEB7Pw36pAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#knn算法\n",
    "print(\"=======================knn算法=========================\")\n",
    "knn = KNeighborsRegressor()\n",
    "knn.fit(x_train,y_train)\n",
    "test_pred = knn.predict(x_test)\n",
    "\n",
    "print(\"预测的上座率为：{}\".format(test_pred[:10]))\n",
    "print(\"实际的上座率为：{}\".format(y_test[:10]))\n",
    "print('均方误差为:',mean_squared_error(y_test, test_pred,sample_weight=None, multioutput='uniform_average'))\n",
    "print(\"平均均对误差:\",mean_absolute_error(y_test, test_pred,sample_weight=None,multioutput='uniform_average'))\n",
    "print(\"knn的平均准确度为:\",knn.score(x_test,y_test))\n",
    "\n",
    "tr = []\n",
    "te = []\n",
    "for i in range(10):\n",
    "    knn = KNeighborsRegressor()\n",
    "    knn = knn.fit(x_train, y_train)\n",
    "    score_tr = knn.score(x_train, y_train)\n",
    "    score_te = cross_val_score(knn,x_test,y_test,cv=10).mean()\n",
    "    tr.append(score_tr)\n",
    "    te.append(score_te)\n",
    "plt.plot(range(1,11),tr,color=\"red\",label=\"train\")\n",
    "plt.plot(range(1,11),te,color=\"blue\",label=\"test\")\n",
    "plt.xticks(range(1,11))\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================决策树算法=========================\n",
      "决策树预测的上座率为： [0.3754717  0.3754717  0.36205882 0.36205882 0.36205882]\n",
      "实际的上座率为： [0.33 0.37 0.37 0.35 0.39]\n",
      "决策树平均准确度： -0.013290881607778138\n",
      "-0.16605958369000307\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#决策树\n",
    "from sklearn import tree#导入模块\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "print(\"=======================决策树算法=========================\")\n",
    "tree = DecisionTreeRegressor(max_depth=3).fit(x_train, y_train)\n",
    "pred_tree = tree.predict(x_test)\n",
    "print(\"决策树预测的上座率为：\",pred_tree[:5])\n",
    "print(\"实际的上座率为：\",y_test[:5])\n",
    "print(\"决策树平均准确度：\",tree.score(x_test,y_test))\n",
    "\n",
    "tr = []\n",
    "te = []\n",
    "for i in range(10):\n",
    "    clf = DecisionTreeRegressor(random_state=25\n",
    "                                ,max_depth = i + 1\n",
    "                                )\n",
    "    clf = clf.fit(x_train, y_train)\n",
    "    score_tr = clf.score(x_train, y_train)\n",
    "    score_te = cross_val_score(clf,x_test,y_test,cv=10).mean()\n",
    "    tr.append(score_tr)\n",
    "    te.append(score_te)\n",
    "print(max(te))\n",
    "plt.plot(range(1,11),tr,color=\"red\",label=\"train\")\n",
    "plt.plot(range(1,11),te,color=\"blue\",label=\"test\")\n",
    "plt.xticks(range(1,11))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================随机森林算法=========================\n",
      "随机森林预测的上座率为： [0.37294695 0.37186206 0.36047433 0.36364482 0.36077753]\n",
      "实际的的上座率为： [0.33 0.37 0.37 0.35 0.39]\n",
      "随机森林平均准确度： 0.10255117015938253\n",
      "-0.1351293953066584\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#随机森林\n",
    "print(\"=======================随机森林算法=========================\")\n",
    "tree = RandomForestRegressor(max_depth=3).fit(x_train, y_train)\n",
    "pred_tree = tree.predict(x_test)\n",
    "print(\"随机森林预测的上座率为：\",pred_tree[:5])\n",
    "print(\"实际的的上座率为：\",y_test[:5])\n",
    "print(\"随机森林平均准确度：\",tree.score(x_test,y_test))\n",
    "tr = []\n",
    "te = []\n",
    "for i in range(10):\n",
    "    rfr = RandomForestRegressor(random_state=25\n",
    "                                ,max_depth = i + 1)\n",
    "    rfr = rfr.fit(x_train, y_train)\n",
    "    score_tr = rfr.score(x_train, y_train)\n",
    "    score_te = cross_val_score(rfr,x_test,y_test,cv=10).mean()\n",
    "    tr.append(score_tr)\n",
    "    te.append(score_te)\n",
    "print(max(te))\n",
    "plt.plot(range(1,11),tr,color=\"red\",label=\"train\")\n",
    "plt.plot(range(1,11),te,color=\"blue\",label=\"test\")\n",
    "plt.xticks(range(1,11))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>航班时刻表</th>\n",
       "      <th>航班号</th>\n",
       "      <th>子订单</th>\n",
       "      <th>日期</th>\n",
       "      <th>头等舱</th>\n",
       "      <th>公务舱</th>\n",
       "      <th>经济舱</th>\n",
       "      <th>其他</th>\n",
       "      <th>头等舱总数</th>\n",
       "      <th>...</th>\n",
       "      <th>经济舱总数</th>\n",
       "      <th>其他总数</th>\n",
       "      <th>高温</th>\n",
       "      <th>低温</th>\n",
       "      <th>天气状况</th>\n",
       "      <th>风</th>\n",
       "      <th>空气</th>\n",
       "      <th>上座人数</th>\n",
       "      <th>总座位数</th>\n",
       "      <th>上座率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CA1876</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>重度污染</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>160.0</td>\n",
       "      <td>CA1880</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>重度污染</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>571.0</td>\n",
       "      <td>CA730</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>重度污染</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2304.0</td>\n",
       "      <td>CA1586</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>重度污染</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>3445.0</td>\n",
       "      <td>CA1868</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>多云</td>\n",
       "      <td>无持续风向微风</td>\n",
       "      <td>重度污染</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523667</th>\n",
       "      <td>922778</td>\n",
       "      <td>925801.0</td>\n",
       "      <td>EK9872</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>晴</td>\n",
       "      <td>北风4-5级</td>\n",
       "      <td>良</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523668</th>\n",
       "      <td>922779</td>\n",
       "      <td>925804.0</td>\n",
       "      <td>EK9872</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>晴</td>\n",
       "      <td>北风4-5级</td>\n",
       "      <td>良</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523669</th>\n",
       "      <td>922780</td>\n",
       "      <td>925805.0</td>\n",
       "      <td>EK9872</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>晴</td>\n",
       "      <td>北风4-5级</td>\n",
       "      <td>良</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523670</th>\n",
       "      <td>922781</td>\n",
       "      <td>926231.0</td>\n",
       "      <td>LD769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>晴</td>\n",
       "      <td>北风4-5级</td>\n",
       "      <td>良</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523671</th>\n",
       "      <td>922782</td>\n",
       "      <td>926232.0</td>\n",
       "      <td>LD769</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>晴</td>\n",
       "      <td>北风4-5级</td>\n",
       "      <td>良</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>523672 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0     航班时刻表     航班号  子订单          日期  头等舱  公务舱  经济舱   其他  \\\n",
       "0                0       1.0  CA1876  0.0  2016-01-03  0.0  0.0  0.0  0.0   \n",
       "1                1     160.0  CA1880  0.0  2016-01-03  0.0  0.0  0.0  0.0   \n",
       "2                2     571.0   CA730  0.0  2016-01-03  0.0  0.0  0.0  0.0   \n",
       "3                5    2304.0  CA1586  0.0  2016-01-03  0.0  0.0  0.0  0.0   \n",
       "4                6    3445.0  CA1868  0.0  2016-01-03  0.0  0.0  0.0  0.0   \n",
       "...            ...       ...     ...  ...         ...  ...  ...  ...  ...   \n",
       "523667      922778  925801.0  EK9872  0.0  2016-11-07  0.0  0.0  0.0  0.0   \n",
       "523668      922779  925804.0  EK9872  3.0  2016-11-07  0.0  0.0  0.0  0.0   \n",
       "523669      922780  925805.0  EK9872  4.0  2016-11-07  0.0  0.0  0.0  0.0   \n",
       "523670      922781  926231.0   LD769  0.0  2016-11-07  0.0  0.0  0.0  0.0   \n",
       "523671      922782  926232.0   LD769  1.0  2016-11-07  0.0  0.0  0.0  0.0   \n",
       "\n",
       "        头等舱总数  ...  经济舱总数  其他总数    高温   低温  天气状况        风    空气 上座人数  总座位数  \\\n",
       "0         0.0  ...    0.0   0.0   4.0 -4.0    多云  无持续风向微风  重度污染  0.0   0.0   \n",
       "1         0.0  ...    0.0   0.0   4.0 -4.0    多云  无持续风向微风  重度污染  0.0   0.0   \n",
       "2         0.0  ...    0.0   0.0   4.0 -4.0    多云  无持续风向微风  重度污染  0.0   0.0   \n",
       "3         0.0  ...    0.0   0.0   4.0 -4.0    多云  无持续风向微风  重度污染  0.0   0.0   \n",
       "4         0.0  ...    0.0   0.0   4.0 -4.0    多云  无持续风向微风  重度污染  0.0   0.0   \n",
       "...       ...  ...    ...   ...   ...  ...   ...      ...   ...  ...   ...   \n",
       "523667    0.0  ...    0.0   0.0  11.0  2.0     晴   北风4-5级     良  0.0   0.0   \n",
       "523668    0.0  ...    0.0   0.0  11.0  2.0     晴   北风4-5级     良  0.0   0.0   \n",
       "523669    0.0  ...    0.0   0.0  11.0  2.0     晴   北风4-5级     良  0.0   0.0   \n",
       "523670    0.0  ...    0.0   0.0  11.0  2.0     晴   北风4-5级     良  0.0   0.0   \n",
       "523671    0.0  ...    0.0   0.0  11.0  2.0     晴   北风4-5级     良  0.0   0.0   \n",
       "\n",
       "        上座率  \n",
       "0       NaN  \n",
       "1       NaN  \n",
       "2       NaN  \n",
       "3       NaN  \n",
       "4       NaN  \n",
       "...     ...  \n",
       "523667  NaN  \n",
       "523668  NaN  \n",
       "523669  NaN  \n",
       "523670  NaN  \n",
       "523671  NaN  \n",
       "\n",
       "[523672 rows x 21 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"df_result_jianmo.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'CA902'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_10472/2940887724.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfeature_selection\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mVarianceThreshold\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mselector\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mVarianceThreshold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mx_var0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mselector\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32md:\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m    697\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    698\u001b[0m             \u001b[1;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 699\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    700\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m             \u001b[1;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\_variance_threshold.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m     66\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m         \"\"\"\n\u001b[1;32m---> 68\u001b[1;33m         X = self._validate_data(X, accept_sparse=('csr', 'csc'),\n\u001b[0m\u001b[0;32m     69\u001b[0m                                 \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     70\u001b[0m                                 force_all_finite='allow-nan')\n",
      "\u001b[1;32md:\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    419\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'no_validation'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 421\u001b[1;33m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    422\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    423\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    671\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    672\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 673\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    674\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    675\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[1;32md:\\anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order, like)\u001b[0m\n\u001b[0;32m    100\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_asarray_with_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlike\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlike\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   1991\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1992\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mNpDtype\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1993\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1994\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1995\u001b[0m     def __array_wrap__(\n",
      "\u001b[1;32md:\\anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order, like)\u001b[0m\n\u001b[0;32m    100\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_asarray_with_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlike\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlike\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'CA902'"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "selector = VarianceThreshold()\n",
    "x_var0 = selector.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 5、整理工程并部署：输入航班号、根据未来一周的天气（网上爬取的天气预报），预测并输出其上座率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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